Chris Fook Sheng Ng∗1, Yutaka Matsuyama∗2 and Yasuo Ohashi∗3 ∗1Department of Human Ecology, School of International Health, Graduate School of Medicine, The University of Tokyo ∗2Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo ∗3Department of Integrated Science and Engineering for Sustainable Society, Faculty of Science and Engineering, Chuo University e-mail:chrisng-tky@umin.ac.jp
{"title":"Case-Only Method to Estimate the Relative Incidence of Adverse Events for Comparison of Two Treatments: Application in Disseminated Intravascular Coagulation Patients","authors":"Chris Fook Sheng Ng, Y. Matsuyama, Y. Ohashi","doi":"10.5691/JJB.36.13","DOIUrl":"https://doi.org/10.5691/JJB.36.13","url":null,"abstract":"Chris Fook Sheng Ng∗1, Yutaka Matsuyama∗2 and Yasuo Ohashi∗3 ∗1Department of Human Ecology, School of International Health, Graduate School of Medicine, The University of Tokyo ∗2Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo ∗3Department of Integrated Science and Engineering for Sustainable Society, Faculty of Science and Engineering, Chuo University e-mail:chrisng-tky@umin.ac.jp","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124956078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The model-based dose-finding method for the combination of two agents consists of the following three components; 1) dose-toxicity model, 2) start-up dose allocation rule before model-based dose-finding stage, and 3) restriction on skipping dose levels in the dose-finding algorithm. Although many authors have developed flexible dose-toxicity models as well as the start-up dose allocation rule, the restriction on skipping dose levels during the trial, has not been adequately studied. In this paper, we propose a new restriction that permits the dropping of dose combinations with toxicity probabilities that are expected to be statistically high, during the trial. We also compared the operating characteristics of the proposed strategy with those of conventional restrictions using simulation studies. Based on the results of the simulation studies, we were able to determine the performance of these strategies and provide some recommendations for their uses.
{"title":"Operating Characteristics of Restrictions on Skipping Dose Level for Adaptive Dose-Finding Method in Two-Agent Phase I Trials","authors":"A. Hirakawa, S. Matsui","doi":"10.5691/JJB.36.1","DOIUrl":"https://doi.org/10.5691/JJB.36.1","url":null,"abstract":"The model-based dose-finding method for the combination of two agents consists of the following three components; 1) dose-toxicity model, 2) start-up dose allocation rule before model-based dose-finding stage, and 3) restriction on skipping dose levels in the dose-finding algorithm. Although many authors have developed flexible dose-toxicity models as well as the start-up dose allocation rule, the restriction on skipping dose levels during the trial, has not been adequately studied. In this paper, we propose a new restriction that permits the dropping of dose combinations with toxicity probabilities that are expected to be statistically high, during the trial. We also compared the operating characteristics of the proposed strategy with those of conventional restrictions using simulation studies. Based on the results of the simulation studies, we were able to determine the performance of these strategies and provide some recommendations for their uses.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126812766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In clinical investigator initiated clinical trials, we frequently encounter the situation where it is very difficult to estimate the effect size and the clinically meaningful difference between the treatment and control groups. In this paper we explore various two-phase, three-stage adaptive designs which can be applied to this situation. The first phase determines whether the trial should proceed or not. If the decision is to proceed, then the sample size is re-estimated. The second phase consists of two stages, but the sample size is not re-estimated. We introduce hybrid and alpha-split designs, adding to two existing adaptive designs: Bauer-Köhne design and Lehmacher-Wassmer design. Main findings are: 1) the differences in the overall powers and the average sample number (ASN)s among these designs are small, except for the design which includes O’Brien-Fleming boundaries and the alpha-split design, 2) the two-phase, three-stage design suffers a relative loss of power by 15% but the ASN is less than 50%, as compared to the single stage design under the optimal condition, 3) two-phase, three-stage design compares with the three-stage group sequential design. We conclude that the design can be a candidate when there is no useful information on the effect size.
{"title":"Two-Phase, Three-Stage Adaptive Designs in Clinical Trials","authors":"H. Uesaka, T. Morikawa, A. Kada","doi":"10.5691/JJB.35.69","DOIUrl":"https://doi.org/10.5691/JJB.35.69","url":null,"abstract":"In clinical investigator initiated clinical trials, we frequently encounter the situation where it is very difficult to estimate the effect size and the clinically meaningful difference between the treatment and control groups. In this paper we explore various two-phase, three-stage adaptive designs which can be applied to this situation. The first phase determines whether the trial should proceed or not. If the decision is to proceed, then the sample size is re-estimated. The second phase consists of two stages, but the sample size is not re-estimated. We introduce hybrid and alpha-split designs, adding to two existing adaptive designs: Bauer-Köhne design and Lehmacher-Wassmer design. Main findings are: 1) the differences in the overall powers and the average sample number (ASN)s among these designs are small, except for the design which includes O’Brien-Fleming boundaries and the alpha-split design, 2) the two-phase, three-stage design suffers a relative loss of power by 15% but the ASN is less than 50%, as compared to the single stage design under the optimal condition, 3) two-phase, three-stage design compares with the three-stage group sequential design. We conclude that the design can be a candidate when there is no useful information on the effect size.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125667461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper considers clinical trials with multiple endpoints, in which the efficacy of a test treatment is confirmed only when the superiority of the test treatment to control is evidenced in at least 1 endpoint and non-inferiority is observed in the remaining endpoints. Perlman and Wu (2004) proposed a one-sided testing procedure that was adaptable to this type of trials. This paper proposes a modification of this procedure, in which the likelihood ratio test is replaced with another test similar to that proposed by Tang et al. (1989). The performance of the proposed procedure was examined through theoretical consideration and Monte Carlo simulations assuming normality and homoscedasticity. The simulation study demonstrated that the power of the proposed procedure was higher than that of the procedure proposed by Perlman and Wu; in this procedure, type I error rates are maintained within nominal significance levels unless primary endpoints are highly correlated.
本文考虑具有多终点的临床试验，其中只有在至少1个终点证明试验治疗优于对照，并且在其余终点观察到非劣效性时，才能确认试验治疗的疗效。Perlman和Wu(2004)提出了一种适用于这类试验的单侧检验程序。本文对这一过程进行了修改，将似然比检验改为类似于Tang et al.(1989)提出的检验。通过理论考虑和假设正态性和均方差的蒙特卡罗模拟来检验所提出程序的性能。仿真研究表明，该算法的求解能力高于Perlman和Wu算法;在此过程中，除非主要终点高度相关，否则I型错误率保持在名义显著性水平内。
{"title":"A New Procedure of One-Sided Test in Clinical Trials with Multiple Endpoints","authors":"Y. Nakazuru, T. Sozu, C. Hamada, I. Yoshimura","doi":"10.5691/JJB.35.17","DOIUrl":"https://doi.org/10.5691/JJB.35.17","url":null,"abstract":"This paper considers clinical trials with multiple endpoints, in which the efficacy of a test treatment is confirmed only when the superiority of the test treatment to control is evidenced in at least 1 endpoint and non-inferiority is observed in the remaining endpoints. Perlman and Wu (2004) proposed a one-sided testing procedure that was adaptable to this type of trials. This paper proposes a modification of this procedure, in which the likelihood ratio test is replaced with another test similar to that proposed by Tang et al. (1989). The performance of the proposed procedure was examined through theoretical consideration and Monte Carlo simulations assuming normality and homoscedasticity. The simulation study demonstrated that the power of the proposed procedure was higher than that of the procedure proposed by Perlman and Wu; in this procedure, type I error rates are maintained within nominal significance levels unless primary endpoints are highly correlated.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123219230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In clinical trials, outcomes of count data sometimes have excess zeros. When a test drug is compared to a control, zero-inflated data may be ignored or interest is taken only in the proportion of zero counts. By applying the two-part model, Lachenbruch (2001a) suggested a test statistic called the two-part statistic that combines the test statistics of the zero part and the non-zero part. The test for the zero part is the chi-square test. The test for the non-zero part may be a Wilcoxon test, a t -test, etc. This article proposes methods for calculating the sample size and power for the two-part statistic with zero-inflated Poisson data. We developed the methods of sample size and power for the two-part statistic using the Wilcoxon test adjusted for ties. The relationship between the non-zero part and zero-truncated Poisson distribution is also described. Furthermore, we examine the power of the two-part statistic, conventional methods, and the zero-inflated Poisson model. in which patients do not recover but have a small value of the outcome that is zero by chance. The zero-inflated Poisson (ZIP) distribution or zero-inflated negative binomial distribution can be applied to count data with excess zeros. This article focuses on the ZIP distribution. The ZIP distribution has two parameters; λ is the Poisson parameter and ω expresses the extent of zero-inflation compared with zero counts that occur from the Poisson distribution.
{"title":"Group Comparisons Involving Zero-Inflated Count Data in Clinical Trials","authors":"K. Togo, Manabu Iwasaki","doi":"10.5691/JJB.34.53","DOIUrl":"https://doi.org/10.5691/JJB.34.53","url":null,"abstract":"In clinical trials, outcomes of count data sometimes have excess zeros. When a test drug is compared to a control, zero-inflated data may be ignored or interest is taken only in the proportion of zero counts. By applying the two-part model, Lachenbruch (2001a) suggested a test statistic called the two-part statistic that combines the test statistics of the zero part and the non-zero part. The test for the zero part is the chi-square test. The test for the non-zero part may be a Wilcoxon test, a t -test, etc. This article proposes methods for calculating the sample size and power for the two-part statistic with zero-inflated Poisson data. We developed the methods of sample size and power for the two-part statistic using the Wilcoxon test adjusted for ties. The relationship between the non-zero part and zero-truncated Poisson distribution is also described. Furthermore, we examine the power of the two-part statistic, conventional methods, and the zero-inflated Poisson model. in which patients do not recover but have a small value of the outcome that is zero by chance. The zero-inflated Poisson (ZIP) distribution or zero-inflated negative binomial distribution can be applied to count data with excess zeros. This article focuses on the ZIP distribution. The ZIP distribution has two parameters; λ is the Poisson parameter and ω expresses the extent of zero-inflation compared with zero counts that occur from the Poisson distribution.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116004574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiple comparisons are attracting increasing attention in the evaluation of statistical evidence in clinical trials including at least one or any combination of (i) multiple hypotheses, (ii) repeated hypotheses testing at interim analyses, and (iii) mid-course design adaptations. In this paper, we discuss an efficient and sensible multiple testing procedure for two-stage adaptive treatment selection designs including structured hypotheses. Specifically, we extend the Holm procedure for serial gatekeeping structured hypotheses in adaptive clinical trials. The proposed approach is based on the idea of combining partition testing with the inverse normal combination test. A clinical trial example is used to illustrate the implementation of the proposed procedure.
{"title":"An Extension of the Holm Procedure Based on Partitioning Principle for Adaptive Treatment Selection Designs with Structured Hypotheses","authors":"Toshifumi Sugitani","doi":"10.5691/JJB.34.67","DOIUrl":"https://doi.org/10.5691/JJB.34.67","url":null,"abstract":"Multiple comparisons are attracting increasing attention in the evaluation of statistical evidence in clinical trials including at least one or any combination of (i) multiple hypotheses, (ii) repeated hypotheses testing at interim analyses, and (iii) mid-course design adaptations. In this paper, we discuss an efficient and sensible multiple testing procedure for two-stage adaptive treatment selection designs including structured hypotheses. Specifically, we extend the Holm procedure for serial gatekeeping structured hypotheses in adaptive clinical trials. The proposed approach is based on the idea of combining partition testing with the inverse normal combination test. A clinical trial example is used to illustrate the implementation of the proposed procedure.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116273389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We construct multiple comparisons procedures in k exponential populations. Exact theory and asymptotic theory of simultaneous confidence intervals and multiple comparisons tests are discussed. First, we consider multiple comparisons for the differences among parameters. We can give the Tukey-Kramer type multiple test procedure based on estimators of k means. However, the degree of conservativeness for the multiple tests depends on unknown mean parameters. Therefore, multiple tests based on the logarithm transformation of estimators are proposed. It is found that the degree of conservativeness for the proposed tests is controlled by the sample sizes. Furthermore, the closed testing procedure, more powerful than the REGW (Ryan/Einot-Gabriel/Welsch) tests, is proposed. Simultaneous confidence intervals for the differences among the logarithms of parameters are discussed. Next, for the multiple comparisons with a control, we propose the multiple test procedures. It is shown that the proposed multiple test is superior to the tests based on the Bonferroni inequality asymptotically. A sequentially rejective procedure is derived under unequal sample sizes. Last, we consider multiple comparisons for all parameters. The exact single-step multiple comparison procedures based on the upper 100α% points the χ2-distribution are proposed. The asymptotic theory for the multiple comparisons is discussed. Especially sequentially rejective procedures can be constructed in the asymptotic theory.
{"title":"Multiple Comparison Procedures in Multi-Sample Exponential Models","authors":"T. Shiraishi","doi":"10.5691/JJB.34.1","DOIUrl":"https://doi.org/10.5691/JJB.34.1","url":null,"abstract":"We construct multiple comparisons procedures in k exponential populations. Exact theory and asymptotic theory of simultaneous confidence intervals and multiple comparisons tests are discussed. First, we consider multiple comparisons for the differences among parameters. We can give the Tukey-Kramer type multiple test procedure based on estimators of k means. However, the degree of conservativeness for the multiple tests depends on unknown mean parameters. Therefore, multiple tests based on the logarithm transformation of estimators are proposed. It is found that the degree of conservativeness for the proposed tests is controlled by the sample sizes. Furthermore, the closed testing procedure, more powerful than the REGW (Ryan/Einot-Gabriel/Welsch) tests, is proposed. Simultaneous confidence intervals for the differences among the logarithms of parameters are discussed. Next, for the multiple comparisons with a control, we propose the multiple test procedures. It is shown that the proposed multiple test is superior to the tests based on the Bonferroni inequality asymptotically. A sequentially rejective procedure is derived under unequal sample sizes. Last, we consider multiple comparisons for all parameters. The exact single-step multiple comparison procedures based on the upper 100α% points the χ2-distribution are proposed. The asymptotic theory for the multiple comparisons is discussed. Especially sequentially rejective procedures can be constructed in the asymptotic theory.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115354888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
寒水孝司∗1・杉本知之∗2・濱崎俊光∗3 Takashi Sozu∗1, Tomoyuki Sugimoto∗2 and Toshimitsu Hamasaki∗3 ∗1京都大学大学院 医学研究科 社会健康医学系専攻 医療統計学 ∗2弘前大学大学院 理工学研究科 数理科学 ∗3大阪大学大学院 医学系研究科 内科系医学専攻 医学統計学 ∗1Department of Biostatistics, Kyoto University School of Public Health ∗2Department of Mathematical Sciences, Hirosaki University Graduate School of Science and Technology ∗3Department of Biomedical Statistics, Osaka University Graduate School of Medicine e-mail:sozu.takashi.4s@kyoto-u.ac.jp
寒水孝司∗1・杉本知之∗2・濱崎俊光∗3 Takashi Sozu∗1, Tomoyuki Sugimoto∗2 and Toshimitsu Hamasaki∗3 ∗1京都大学大学院 医学研究科 社会健康医学系専攻 医療統計学 ∗2弘前大学大学院 理工学研究科 数理科学 ∗3大阪大学大学院 医学系研究科 内科系医学専攻 医学統計学 ∗1Department of Biostatistics, Kyoto University School of Public Health ∗2Department of Mathematical Sciences, Hirosaki University Graduate School of Science and Technology ∗3Department of Biomedical Statistics, Osaka University Graduate School of Medicine e-mail:sozu.takashi.4s@kyoto-u.ac.jp
{"title":"Statistical Issues in Clinical Trials with Multiple Primary Endpoints","authors":"T. Sozu, Tomoyuki Sugimoto, T. Hamasaki","doi":"10.5691/JJB.34.35","DOIUrl":"https://doi.org/10.5691/JJB.34.35","url":null,"abstract":"寒水孝司∗1・杉本知之∗2・濱崎俊光∗3 Takashi Sozu∗1, Tomoyuki Sugimoto∗2 and Toshimitsu Hamasaki∗3 ∗1京都大学大学院 医学研究科 社会健康医学系専攻 医療統計学 ∗2弘前大学大学院 理工学研究科 数理科学 ∗3大阪大学大学院 医学系研究科 内科系医学専攻 医学統計学 ∗1Department of Biostatistics, Kyoto University School of Public Health ∗2Department of Mathematical Sciences, Hirosaki University Graduate School of Science and Technology ∗3Department of Biomedical Statistics, Osaka University Graduate School of Medicine e-mail:sozu.takashi.4s@kyoto-u.ac.jp","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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